Bewerbende mit Behinderungen erwünscht 👨‍🦽👨‍🦯🦻 Master Thesis for dynamic surrogate model development for a physics-based fuel cell system model using SINDy (d/f/m)

Job Description:

In order to support the Fuel Cell Systems Team within the ZEROe project, Airbus Operations is looking for a

Master Thesis student for dynamic surrogate model development for a physics-based fuel cell system model using SINDy (d/f/m)

You are looking for a master thesis in a research and development environment and want to work on Fuel Cell Systems and Modeling/Simulation? Then apply now! We look forward to you supporting us in the Fuel Cell Systems team as a Masters student (d/f/m)!

• Location: Hamburg
  
• Start: 01.10.2025
  
• Duration: 6 months
  

Proton Exchange Membrane (PEM) fuel cells enable an efficient and locally emission free conversion from hydrogen to electrical energy. This is why they are considered as important technologies to tackle the necessary mobility transition towards a sustainable aviation industry.

In the systems development approach physics-based models are capable to depict technical systems for a wide range of operating conditions. But they are too computationally demanding for certain applications (e.g. controls development) and can be challenging to cross-integrate between different tools. It is possible to build surrogate models to tackle these shortcomings. SINDy is one method that appears to be powerful in identifying the characteristic equations describing such a system as it has already given promising results on fuel cell stack / component level.

The main challenges compared to the component level will be the use of non-continuous controls inputs (e.g. valve open or closed) instead of continuous physical inputs (e.g. pressure) and a more challenging data generation. Additionally, the scope of depiction is larger.

Thus, SINDy shall be applied to a detailed physics-based fuel cell system model. Time series data needs to be generated in a sensible way to capture at least most of the relevant characteristics of the depicted component. From this data a surrogate model shall be built using SINDy. This model shall be evaluated with regards to computational performance, prediction accuracy and its capability to extrapolate. Possible ways to re-integrate the surrogate model into the platform of the original reference model shall be examined. Optionally, qualitatively different transient cases like the dynamics of a start-up or shut-down could be evaluated.

Your location

You will be working at one of the largest production sites for civil aircraft situated in Hamburg, Germany. Its location on the southern banks of the river Elbe includes the option to commute by ferry. Experience the special flair of Hamburg in your spare time where vibrant cosmopolitan culture meets nautic legacy.

Your benefits

• Work-life balance with a 35-hour week (flexitime).
  
• Mobile working after agreement with the department.
  
• Traveling within Germany (team events) is possible after consultation and agreement from the department.
  
• International environment with the opportunity to network globally.
  
• Work with modern/diversified technologies.
  
• At Airbus, we see you as a valuable team member and you are not hired to brew coffee, instead you are in close contact with the interfaces and are part of our weekly team meetings.
  
• Opportunity to participate in the Generation Airbus Community to expand your own network.
  

Your tasks and responsibilities

• Literature review on time series prediction using SINDy and a fuel cell systems main characteristics and dynamics
  
• Data generation using the physics based model including the identification of the necessary inputs, outputs and transient excitements to extract models behavioral characteristics
  
• Further development and adaptation of an existing Python framework to:
  
  • read and process multivariate time series data
    
  • generate SINDy models
    
  • handle controls inputs in addition to continuous physical inputs
    
  • evaluate model performance
    
• Integration of the surrogate model into target platform (Dymola)
  
• Evaluation of the surrogate models capabilities with regards to
  
  • Computational performance
    
  • Prediction accuracy
    
  • Extrapolation (with regards to input values and input dynamics)
    
  • Optional: Start-up & shut-down capabilities using a SINDy-based model
    
• Discussion of the observed results
  

Desired skills and qualifications

• Enrolled full-time student (d/f/m) in the area of Electrical Engineering, Mechanical Engineering, Chemistry, Renewable Energy Systems or an equivalent field of study.
  
• Advanced experience in the field of modeling and simulation of systems or components. A first contact with fuel cell systems or fuel cell stacks or hydrogen systems is desirable but not necessary.
  
• Fluent in English, German language knowledge is a plus.
  
• Good knowledge of thermo-dynamics, electrical power systems, or electro-chemical processes.
  

Please upload the following documents: cover letter, CV, relevant transcripts, enrollment certificate.

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This job requires an awareness of any potential compliance risks and a commitment to act with integrity, as the foundation for the Company’s success, reputation and sustainable growth.

Company:
Airbus Operations GmbH

Employment Type:
Final-year Thesis
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Experience Level:
Student

Job Family:

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